Comparing temperature data sources for use in species distribution models: From in‐situ logging to remote sensing
|Author(s)||Lembrechts Jonas J.1, Lenoir Jonathan2, Roth Nina3, Hattab Tarek2, 4, Milbau Ann5, Haider Sylvia6, 7, Pellissier Loïc8, 9, Pauchard Aníbal10, 11, Ratier Backes Amanda6, 7, Dimarco Romina D.12, Nuñez Martin A.13, Aalto Juha14, 15, Nijs Ivan1, Bates Amanda|
|Affiliation(s)||1 : Centre of Excellence Plants and Ecosystems (PLECO) University of Antwerp Wilrijk, Belgium
2 : UR “Ecologie et Dynamique des Systèmes Anthropisés” (EDYSAN, UMR 7058 CNRS‐UPJV) Université de Picardie Jules Verne Amiens Cedex 1 ,France
3 : Biogeography and Geomatics, Department of Physical Geography Stockholm University Stockholm ,Sweden
4 : MARBEC (IRD, Ifremer, Université de Montpellier, CNRS) Sète Cedex ,France
5 : Research Institute for Nature and Forest – INBO Brussels, Belgium
6 : Institute of Biology/Geobotany and Botanical Garden Martin Luther University Halle‐Wittenberg Halle (Saale) ,Germany
7 : German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig, Germany
8 : Landscape Ecology, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science ETH Zürich Zürich ,Switzerland
9 : Swiss Federal Research Institute WSL Birmensdorf ,Switzerland
10 : Laboratorio de Invasiones Biológicas, Facultad de Ciencias Forestales Universidad de Concepción Concepción ,Chile
11 : Institute of Ecology and Biodiversity (IEB) Santiago, Chile
12 : Grupo de Ecología de Poblaciones de Insectos INTA‐CONICET Bariloche, Argentina
13 : Grupo de Ecología de Invasiones INIBIOMA, CONICET‐Universidad Nacional del Comahue Bariloche ,Argentina
14 : The Department of Geosciences and Geography FIN‐00014 University of Helsinki Helsinki, Finland
15 : Finnish Meteorological Institute Helsinki, Finland
|Source||Global Ecology And Biogeography (1466-822X) (Wiley), 2019-11 , Vol. 28 , N. 11 , P. 1578-1596|
|WOS© Times Cited||79|
|Keyword(s)||bioclimatic envelope modelling, bioclimatic variables, climate change, growth forms, land surface temperature, microclimate, mountains, soil temperature, species distribution modelling|
Although species distribution models (SDMs) traditionally link species occurrences to free‐air temperature data at coarse spatio‐temporal resolution, the distribution of organisms might instead be driven by temperatures more proximal to their habitats. Several solutions are currently available, such as downscaled or interpolated coarse‐grained free‐air temperatures, satellite‐measured land surface temperatures (LST) or in‐situ‐measured soil temperatures. A comprehensive comparison of temperature data sources and their performance in SDMs is, however, currently lacking.
Major taxa studied
We evaluated different sources of temperature data (WorldClim, CHELSA, MODIS, E‐OBS, topoclimate and soil temperature from miniature data loggers), differing in spatial resolution (from 1″ to 0.1°), measurement focus (free‐air, ground‐surface or soil temperature) and temporal extent (year‐long versus long‐term averages), and used them to fit SDMs for 50 plant species with different growth forms in a high‐latitudinal mountain region.
Differences between these temperature data sources originating from measurement focus and temporal extent overshadow the effects of temporal climatic differences and spatio‐temporal resolution, with elevational lapse rates ranging from −0.6°C per 100 m for long‐term free‐air temperature data to −0.2°C per 100 m for in‐situ soil temperatures. Most importantly, we found that the performance of the temperature data in SDMs depended on the growth forms of species. The use of in‐situ soil temperatures improved the explanatory power of our SDMs (R2 on average +16%), especially for forbs and graminoids (R2 +24 and +21% on average, respectively) compared with the other data sources.
We suggest that future studies using SDMs should use the temperature dataset that best reflects the ecology of the species, rather than automatically using coarse‐grained data from WorldClim or CHELSA.